code: def kahypar_cut(
      graph: MultiDiGraph,
      num_fragments: int,
      imbalance: int = None,
      edge_weights: List[Union[int, float]] = None,
      node_weights: List[Union[int, float]] = None,
      fragment_weights: List[Union[int, float]] = None,
      hyperwire_weight: int = 1,
      seed: int = None,
      config_path: Union[str, Path] = None,
      trial: int = None,
      verbose: bool = False,
  ) -> List[Tuple[Operation, Operation, Any]]:
      """Calls `KaHyPar <https://kahypar.org/>`__ to partition a graph.
  
      .. warning::
          Requires KaHyPar to be installed separately. For Linux and Mac users,
          KaHyPar can be installed using ``pip install kahypar``. Windows users
          can follow the instructions
          `here <https://kahypar.org>`__ to compile from source.
  
      Args:
          graph (nx.MultiDiGraph): The graph to be partitioned.
          num_fragments (int): Desired number of fragments.
          imbalance (int): Imbalance factor of the partitioning. Defaults to KaHyPar's determination.
          edge_weights (List[Union[int, float]]): Weights for edges. Defaults to unit-weighted edges.
          node_weights (List[Union[int, float]]): Weights for nodes. Defaults to unit-weighted nodes.
          fragment_weights (List[Union[int, float]]): Maximum size constraints by fragment. Defaults
              to no such constraints, with ``imbalance`` the only parameter affecting fragment sizes.
          hyperwire_weight (int): Weight on the artificially appended hyperedges representing wires.
              Setting it to 0 leads to no such insertion. If greater than 0, hyperedges will be
              appended with the provided weight, to encourage the resulting fragments to cluster gates
              on the same wire together. Defaults to 1.
          seed (int): KaHyPar's seed. Defaults to the seed in the config file which defaults to -1,
              i.e. unfixed seed.
          config_path (str): KaHyPar's ``.ini`` config file path. Defaults to its SEA20 paper config.
          trial (int): trial id for summary label creation. Defaults to ``None``.
          verbose (bool): Flag for printing KaHyPar's output summary. Defaults to ``False``.
  
      Returns:
          List[Union[int, Any]]: List of cut edges.
  
      **Example**
  
      Consider the following 2-wire circuit with one CNOT gate connecting the wires:
  
      .. code-block:: python
  
          ops = [
              qml.RX(0.432, wires=0),
              qml.RY(0.543, wires="a"),
              qml.CNOT(wires=[0, "a"]),
              qml.RZ(0.240, wires=0),
              qml.RZ(0.133, wires="a"),
              qml.RX(0.432, wires=0),
              qml.RY(0.543, wires="a"),
          ]
          measurements = [qml.expval(qml.PauliZ(wires=[0]))]
          tape = qml.tape.QuantumTape(ops, measurements)
  
      We can let KaHyPar automatically find the optimal edges to place cuts:
  
      >>> graph = qml.qcut.tape_to_graph(tape)
      >>> cut_edges = qml.qcut.kahypar_cut(
              graph=graph,
              num_fragments=2,
          )
      >>> cut_edges
      [(CNOT(wires=[0, 'a']), RZ(0.24, wires=[0]), 0)]
      """
      # pylint: disable=too-many-arguments, import-outside-toplevel
      try:
          import kahypar
      except ImportError as e:
          raise ImportError(
              "KaHyPar must be installed to use this method for automatic "
              "cut placement. Try pip install kahypar or visit "
              "https://kahypar.org/ for installation instructions."
          ) from e
  
      adjacent_nodes, edge_splits, edge_weights = _graph_to_hmetis(
          graph=graph,
          hyperwire_weight=hyperwire_weight,
          edge_weights=edge_weights,
      )
  
      trial = 0 if trial is None else trial
      ne = len(edge_splits) - 1
      nv = max(adjacent_nodes) + 1
  
      if edge_weights is not None or node_weights is not None:
          edge_weights = edge_weights or [1] * ne
          node_weights = node_weights or [1] * nv
          hypergraph = kahypar.Hypergraph(
              nv,
              ne,
              edge_splits,
              adjacent_nodes,
              num_fragments,
              edge_weights,
              node_weights,
          )
  
      else:
          hypergraph = kahypar.Hypergraph(
              nv, ne, edge_splits, adjacent_nodes, num_fragments
          )
  
      context = kahypar.Context()
  
      config_path = config_path or str(
          Path(__file__).parent / "_cut_kKaHyPar_sea20.ini"
      )
      context.loadINIconfiguration(config_path)
  
      context.setK(num_fragments)
  
      if isinstance(imbalance, float):
          context.setEpsilon(imbalance)
      if isinstance(fragment_weights, SequenceType) and (
          len(fragment_weights) == num_fragments
      ):
          context.setCustomTargetBlockWeights(fragment_weights)
      if not verbose:
          context.suppressOutput(True)
  
      # KaHyPar fixes seed to 42 by default, need to manually sample seed to randomize:
      kahypar_seed = np.random.default_rng(seed).choice(2**15)
      context.setSeed(kahypar_seed)
  
      kahypar.partition(hypergraph, context)
  
      cut_edge_mask = [
          hypergraph.connectivity(e) > 1 for e in hypergraph.edges()
      ]
  
      # compress() ignores the extra hyperwires at the end if there is any.
      cut_edges = list(compress(graph.edges, cut_edge_mask))
  
      if verbose:
          fragment_sizes = [
              hypergraph.blockSize(p) for p in range(num_fragments)
          ]
          print(len(fragment_sizes), fragment_sizes)
  
      return cut_edges
  
file_path: pennylane/pennylane/qcut/kahypar.py
imports: from collections.abc import Sequence
  from itertools import compress
  from pathlib import Path
  from typing import Any, List, Sequence, Tuple, Union
  import numpy
  from networkx import MultiDiGraph
  import pennylane
  from pennylane.operation import Operation
label_desc: Use image node_id 1 for calling a global function with example usage: kahypar_cut(graph, num_fragments, imbalance, edge_weights, node_weights, fragment_weights, hyperwire_weight, seed, config_path, trial, verbose) and returns: cut_edges